Endoscopic endonasal surgery (EES) is a common procedure for treating pituitary lesions and has proven to be effective in reducing tissue trauma and speeding up the recovery time. However, due to the reduced workspace and the lack of direct visualization, much dexterity is required for the execution of complex surgical tasks with increased surgeon’s work effort. Suturing is considered one of the most challenging and time-consuming tasks in minimally invasive surgery [
1], and is a common procedure in EES to secure the reconstructed dura after an endonasal tumor resection (see
Figure 1). Inadequate suture can lead to cerebrospinal fluid leakage, which is a common postoperative complication in EES. The limited degrees of freedom (DOFs) in conventional surgical tools reduce the range of motion for needle manipulation. Furthermore, the two-dimensional endoscopic view makes it difficult to recognize the relative position of the tissue from the surgical instruments, resulting in multiple attempts and increased tissue trauma to achieve a proper suture [
2].
Recently, there has been an increasing interest on the development of robotic surgical systems to perform complex tasks such as suturing in a limited workspace with reduced trauma, by autonomous procedures with higher dexterity [
3]. Robotic systems can also help to reduce the steep learning curve for surgeons associated with minimally invasive suturing. However, the development of commercial robotic surgical systems for EES is still limited. For example, the da Vinci Surgical System (Intuitive Surgical, Sunnyvale, CA, USA) has been used successfully in urological, gynecological, and gastrointestinal surgery. The use of their high-dexterity endo-wrist technology facilitates the manipulation of surgical needles on a limited workspace. However, the size of the current surgical instruments (5–8 mm in shaft diameter), the large required workspace, and complex preoperational setup still prevent the use of the da Vinci Surgical System in EES.
1.1. Related Work
A suturing task comprises several steps: (s0) selection of suitable entry and exit points; (s1) needle grasping, placement, and reorientation over the entry point; (s2) needle insertion and extraction; (s3) create a suture loop for knot tying; and (s4) tighten and secure the knot [
5]. In minimally invasive surgery, suturing is a frequent, repetitive, and yet time-consuming task. Previous studies have focused on the automation of one or some of the above-mentioned steps in the suturing task in order to reduce surgeon’s fatigue and operation time.
Learning-by-demonstration techniques have been proposed for autonomous knot-tying tasks (s3–s4). Knoll et al. [
6] introduced a skill transfer approach from human demonstrations based on knot-tying primitives decomposition, feature extraction, and task generalization using the da Vinci robot. However, the implementation was not suitable for online planning because of the significant computational cost. In [
7], Osa et al. use a set of demonstrated trajectories under various environmental conditions to learn the knot-tying process, and proposed an online trajectory regeneration for adapting over changes of the dynamic surgical environment. On the other hand, Van der Berg et al. [
8] proposed the use of iterative learning control to determine a task trajectory without the need of the task description. Different approaches have been proposed to perform autonomous surgical tasks without learning. Multi-step sequential trajectory specifically designed for knot-tying was proposed in [
9]. Chow et al. [
10] proposed a knot-tying automated path generation based on a binary-star search method over objective metrics defined for candidate motion patterns.
Autonomous stitching (s0–s2) has also been studied, mainly focused on the generation of an optimal needle trajectory, and can be divided into two types: constant curvature paths and adjustable curvature paths. In the case of a constant curvature path, the needle rotates around its center to reduce trauma when puncturing the tissue. Nageotte et al. [
11] presented a kinematic analysis of the stitching task, and used an A*-based method to find the optimal needle path given the desired entry and exit points. Liu et al. [
12] introduced an offline optimization framework for optimal entry port selection and needle grasping pose. However, the exhaustive search methods require a high computational cost and is not suitable for real-time implementation. Staub et al. [
13] proposed a visual-servoing control to position the needle over a desired point marked by a laser pointer and performs a circular needle motion to pierce the tissue. D’Ettore et al. [
14] also applied a vision based control for autonomous needle grasping, but did not consider additional requirements for subsequents suturing steps. Iyer et al. [
15] proposed a visual-servoing control for autonomous stitching, which provides smooth needle steering by calculating the needle optimal center point of rotation. Pedram et al. [
16] used a nonlinear optimization algorithm to generate a needle constant curvature path subject to the tissue geometry, desired entry/exit points and kinematic constraints.
Notice that the use of a constant curvature paths could fail to meet the suturing requirements, e.g., depth and length, or conflict with the robot kinematic constraints. As a result, capability of exerting forces required for tissue penetration could be reduced. Therefore, subsequent studies have considered adjustable curvature paths, where needle orientation adjustments are allowed. Sen et al. [
17] proposed a sequential non-convex optimization framework to find the optimal trajectory, subject to kinematic constraints, bounded needle reorientation, minimum trajectory length, and orthogonal needle poses to reduce tissue trauma. It also includes a mechanical needle guide to reduce needle pose uncertainty. Jackson et al. [
18] developed a needle trajectory plan based on the best practices of manual suturing that allows needle reorientation and ensures suture depth and needle handling. Autonomous needle extraction was proposed in [
19], in which the visual feedback is used to control two teleoperated robot arms with a single user interface.
Additional studies explored the automation of other surgical sub-tasks: optimal port placement [
20], surgical debridement [
21], surgical cutting [
22,
23], and real-time thread tracking [
24,
25]. Fully autonomous suturing is still considered a high-risk procedure because of the variability in human anatomy and uncertainties in environment modeling (tissue, needle pose, and thread). They also rely on time-consuming complex calibration setups that need frequent readjustments and low operational speeds that extend the operation time.
Cooperative human–robot suturing with shared control between the operator and robot has been also explored as an alternative to autonomous suturing. Here, the robot can guide or restrict the surgeon’s command, or execute automated surgical sub-tasks. In [
26], the surgeon commands the robot in subtasks where environment interactions are involved, such as grasping or needle insertion, and automatic execution of pre-learned subtasks are executed sequentially after the manual subtasks. Reed et al. [
27] developed a robot-assisted steering system for bevel-tip steerable needles by integrating a stochastic roadmap-based planer, a planar controller, and a torsion compensator. During the surgical procedure, the surgeon can either only pause the insertion to verify the needle location or abort the procedure by retracting the needle.
Virtual constraints are often used to guide or constrain the surgeon’s commanding motion. Kapoor et al. [
5] proposed a guidance virtual fixture to assist the surgeon move towards the entry point and along the desired trajectory defined for a stitching task. The constrained motions is formulated as a quadratic programming minimization problem. The surgeon commands the robot through a force-based control, and needle reorientation is allowed to reduce the error between the desired entry/exit points. An impedance virtual fixture framework for needle passing and knot tying was introduced in [
28] by constraining the tool tip within a plane, and reducing needle pose uncertainty through a 3D printed needle holder. Selvaggio et al. [
29] presented an optimization-based haptic shared control for needle grasping that takes into consideration the robot joint limits and singularities. Marinho et al. [
30] developed a looping guidance virtual fixture and a trajectory guidance cylinder based on constrained optimization and haptic feedback to assist the surgeon during a teleoperated knot-tying task. In [
31], multiple control strategies for a stitching task were compared: telemanipulation, autonomous, shared control with orientation free, and shared control with orientation constrained.
In this work, we propose an optimization-based needle trajectory planning for a stitching task considering the suturing restrictions for an EES. The contributions of this paper can be summarized as follows.
An online optimization-based needle trajectory generation method that is used as a reference for a smooth guidance virtual fixture.
Constrained motion planning based on dual concurrent inverse kinematics (IK) solver that integrates a task-priority based IK and a nonlinear optimization based IK.
Experimental comparison between the proposed method in a robot-assisted mode and an autonomous mode with the use of a conventional surgical tool.